With the development of deep learning algorithms, more and more deep learning algorithms are being applied to remote sensing image classification, detection, and semantic segmentation. The landslide semantic segmentation of a remote sensing image based on deep learning mainly uses supervised learning, the accuracy of which depends on a large number of training data and high-quality data annotation. At this stage, high-quality data annotation often requires the investment of significant human effort. Therefore, the high cost of remote sensing landslide image data annotation greatly restricts the development of a landslide semantic segmentation algorithm. Aiming to resolve the problem of the high labeling cost of landslide semantic segmentation with a supervised learning method, we proposed a remote sensing landslide semantic segmentation with weakly supervised learning method combing class activation maps (CAMs) and cycle generative adversarial network (cycleGAN). In this method, we used the image level annotation data to replace pixel level annotation data as the training data. Firstly, the CAM method was used to determine the approximate position of the landslide area. Then, the cycleGAN method was used to generate the fake image without a landslide, and to make the difference with the real image to obtain the accurate segmentation of the landslide area. Finally, the pixel-level segmentation of the landslide area on remote sensing image was realized. We used mean intersection-over-union (mIOU) to evaluate the proposed method, and compared it with the method based on CAM, whose mIOU was 0.157, and we obtain better result with mIOU 0.237 on the same test dataset. Furthermore, we made a comparative experiment using the supervised learning method of a u-net network, and the mIOU result was 0.408. The experimental results show that it is feasible to realize landslide semantic segmentation in a remote sensing image by using weakly supervised learning. This method can greatly reduce the workload of data annotation.

Many objects in the naturalenvironment are generated from the background and even transformed by nature or human beings. Thus, they do not have closed and well-defined boundaries in remote sensing imagery. Recently, convolutional neural network (CNN) based object detection achieved great success in the remote sensing field. However, there is no investigation in the literature about the detection of objects with ambiguous boundaries. In this article, taking the case of the potential loess landslide detection, we designed massive experiments to evaluate convolutional neural networks for detecting objects with ambiguous boundaries in remote sensing imagery. We analyzed the evaluated methods comprehensively by comparing the performance on objects with ambiguous boundaries in remote sensing imagery with the performance on ordinary objects in visual imagery. Furthermore, drawing from these analyses, we provided a fundamental principle of object representation and a meaningful suggestion of information learning to detect objects with ambiguous boundaries. We finished this article by presenting several promising directions for detecting objects with ambiguous boundaries to facilitate and spur future research. This article would provide a significant reference and guidance to develop detectors for objects with ambiguous boundaries in remote sensing imagery.

This paper proposes a novel high-sensitivity micro-electromechanical system (MEMS) piezoresistive pressure sensor that can be used for rock mass stress monitoring. The entire sensor consists of a cross, dual-cavity, and all-silicon bulk-type (CCSB) structure. Firstly, the theoretical analysis is carried out, and the relationship between the structural parameters of the sensor and the stress is analyzed by finite element simulation and curve-fitting prediction, and then the optimal structural parameters are also analyzed. The simulation results indicate that the sensor with the CCSB structure proposed in this article obtained a high sensitivity of 87.74 μV/V/MPA and a low nonlinearity error of 0.28% full-scale span (FSS) within the pressure range of 0–200 MPa. Compared with All-Si Bulk, grooved All-Si Bulk, Si-Glass Bulk, silicon diaphragm, resistance strain gauge, and Fiber Bragg grating structure pressure sensors, the designed sensor has a significant improvement in sensitivity and nonlinearity error. It can be used as a new sensor for rock disaster (such as collapse) monitoring and forecasting.

Multi-objective optimization is a cornerstone of modern engineering and management, tackling challenges in complex system design, resource allocation, and financial portfolio optimization. Effective multi-objective optimization algorithms must strike a balance between convergence and diversity, a process that inherently reflects the symmetry in objectives and their trade-offs. However, real-world complexities introduce significant hurdles: the exponential increase in Pareto optimal solutions diminishes the effectiveness of dominance-based selection, while escalating problem complexity heightens the tension between convergence and diversity. To address these issues, we propose a two-archive evolutionary algorithm that integrates reference vectors and a novel hypervolume contribution strategy. This approach employs two complementary archives—convergence and diversity—for parallel optimization. Within the diversity archive, candidate solutions are first filtered using angular relationships to maintain uniform distribution. A novel hypervolume contribution evaluation strategy (HVindex) then determines whether updating solutions can improve the overall quality of the neighborhood population. For the convergence archive, we first select all the non-dominated solutions through non-dominated sorting. These solutions are further refined using reference vectors, and the final archive is completed by adding some opposite characteristic solutions based on distance measurements. The experimental results demonstrate that the proposed algorithm outperforms existing methods in multi-objective optimization.